Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network

Abstract

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.

DOI: 10.7150/ijbs.8430

Extracted Key Phrases

10 Figures and Tables

Cite this paper

@inproceedings{Yang2014PredictingDP, title={Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network}, author={Lei Yang and Xudong Zhao and Xianglong Tang}, booktitle={International journal of biological sciences}, year={2014} }